15,139 research outputs found
GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA
This work presents an updated and extended guide on methods of a proper
acceleration of the Monte Carlo integration of stochastic differential
equations with the commonly available NVIDIA Graphics Processing Units using
the CUDA programming environment. We outline the general aspects of the
scientific computing on graphics cards and demonstrate them with two models of
a well known phenomenon of the noise induced transport of Brownian motors in
periodic structures. As a source of fluctuations in the considered systems we
selected the three most commonly occurring noises: the Gaussian white noise,
the white Poissonian noise and the dichotomous process also known as a random
telegraph signal. The detailed discussion on various aspects of the applied
numerical schemes is also presented. The measured speedup can be of the
astonishing order of about 3000 when compared to a typical CPU. This number
significantly expands the range of problems solvable by use of stochastic
simulations, allowing even an interactive research in some cases.Comment: 21 pages, 5 figures; Comput. Phys. Commun., accepted, 201
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Fast, non-monte-carlo estimation of transient performance variation due to device mismatch
This paper describes an efficient way of simulating the effects of device random mismatch on circuit transient characteristics, such as variations in delay or in frequency. The proposed method models DC random offsets as equivalent AC pseudo-noises and leverages the fast, linear periodically time-varying (LPTV) noise analysis available from RF circuit simulators. Therefore, the method can be considered as an extension to DC match analysis and offers a large speed-up compared to the traditional Monte-Carlo analysis. Although the assumed linear perturbation model is valid only for small variations, it enables easy ways to estimate correlations among variations and identify the most sensitive design parameters to mismatch, all at no additional simulation cost. Three benchmarks measuring the variations in the input offset voltage of a clocked comparator, the delay of a logic path, and the frequency of an oscillator demonstrate the speed improvement of about 100-1000x compared to a 1000-point Monte-Carlo method
Regression Monte Carlo for Microgrid Management
We study an islanded microgrid system designed to supply a small village with
the power produced by photovoltaic panels, wind turbines and a diesel
generator. A battery storage system device is used to shift power from times of
high renewable production to times of high demand. We introduce a methodology
to solve microgrid management problem using different variants of Regression
Monte Carlo algorithms and use numerical simulations to infer results about the
optimal design of the grid.Comment: CEMRACS 2017 Summer project - proceedings
Increasing throughput in IEEE 802.11 by optimal selection of backoff parameters
Engineering and Physical Sciences Research Council. Grant Number: EP/G012628/
Cluster Monte Carlo Algorithms for Dissipative Quantum Systems
We review efficient Monte Carlo methods for simulating quantum systems which
couple to a dissipative environment. A brief introduction of the
Caldeira-Leggett model and the Monte Carlo method will be followed by a
detailed discussion of cluster algorithms and the treatment of long-range
interactions. Dissipative quantum spins and resistively shunted Josephson
junctions will be considered.Comment: to be publushed in Proceedings of the Yukawa Symposium 200
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